Various imaging systems have been proposed that photographically capture images of a person's face for analysis of the health and aesthetic appearance of the skin. Different images, e.g., captured at different times or under different lighting conditions could be compared to one another to gain insight into the condition of the skin, e.g., at different times, such as before and after treatment, in order to ascertain trends in the condition of the skin. This was typically done by human operators inspecting the photographs to ascertain changes between them, based on color, texture, etc. In analyzing the skin of a person's face, it is beneficial to examine specific regions of the face for specific associated attributes, since the different regions of the face are specialized in form and function and interact with the environment differently. For example, the skin covering the nose is exposed to the most direct and intense rays of the sun, i.e., those emitted from late morning to early afternoon and therefore has a greater number of sebaceous glands and pores to provide skin oils to prevent the skin of the nose from burning and drying out. In contrast, the skin of the eyelids is shielded from the sun due to the bunching of the eyelid and retraction into the eye socket when the eye is open. Unlike the skin of the nose or cheek regions, the eyelids must be thin and flexible with numerous folds to facilitate the rapid opening and closing of the eye.
Because imaging is now usually conducted with a digital camera, the resultant images are subject to quantitative analysis. Quantitative image analysis is more informative if conducted recognizing the specialization of skin in different facial regions. Some skin imaging systems utilize a trained human operator to identify facial regions by manually touching (on a touch-sensitive input/output screen) or pointing to (with a cursor and clicking or indicating) fiducial points on a displayed facial image. Alternatively, polygons may be drawn on an image (with a cursor/mouse or stylus) to identify the facial regions of interest. For example, the cheek area could be denoted using lines connecting facial fiducial reference points such as the corner of the nose, the corner of the lip, the ear, the lateral edge of the eye and back to the corner of the nose. While effective, such manual operations are labor intensive and require trained operators. It would therefore be beneficial to identify facial regions on images automatically to increase the speed and consistency of identification of the facial regions and to decrease the reliance upon operator input.